Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10997991 | Journal of Computational Science | 2016 | 32 Pages |
Abstract
In this paper we introduce the Multiobjective Optimization Hierarchic Genetic Strategy with maturing (MO-mHGS), a meta-algorithm that performs evolutionary optimization in a hierarchy of populations. The maturing mechanism improves growth and reduces redundancy. The performance of MO-mHGS with selected state-of-the-art multiobjective evolutionary algorithms as internal algorithms is analysed on benchmark problems and their modifications for which single fitness evaluation time depends on the solution accuracy. We compare the proposed algorithm with the Island Model Genetic Algorithm as well as with single-deme methods, and discuss the impact of internal algorithms on the MO-mHGS meta-algorithm.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
RadosÅaw Åazarz, MichaÅ Idzik, Konrad GÄ
dek, Ewa Gajda-Zagórska,